





"""simple docstring"""







import os

import numpy
import onnx







def     A__			(       __lowerCamelCase,		__lowerCamelCase					):





  """simple docstring"""
  _lowerCAmelCase	      =					a.name
  _lowerCAmelCase	      =					b.name

  _lowerCAmelCase	      =					''
  _lowerCAmelCase	      =					''

  _lowerCAmelCase	      =					a == b

  _lowerCAmelCase	      =					name_a
  _lowerCAmelCase	      =					name_b

  return res







def     A__			(       __lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase					):





  """simple docstring"""
  for i, input_name in enumerate(node_proto.input					):
    if input_name == name:
      node_proto.input.insert(UpperCAmelCase_,		UpperCAmelCase_					)
      node_proto.input.pop(i + 1					)

  if node_proto.op_type == "If":
    _graph_replace_input_with(node_proto.attribute[0].g,		UpperCAmelCase_,		UpperCAmelCase_					)
    _graph_replace_input_with(node_proto.attribute[1].g,		UpperCAmelCase_,		UpperCAmelCase_					)
  if node_proto.op_type == "Loop":
    _graph_replace_input_with(node_proto.attribute[0].g,		UpperCAmelCase_,		UpperCAmelCase_					)







def     A__			(       __lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase					):





  """simple docstring"""
  for n in graph_proto.node:
    _node_replace_input_with(UpperCAmelCase_,		UpperCAmelCase_,		UpperCAmelCase_					)







def     A__			(       __lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase					):





  """simple docstring"""
  _lowerCAmelCase	      =					list(model.graph.initializer					)
  _lowerCAmelCase	      =					list(model_without_ext.graph.initializer					)
  for i, ref_i in ind_to_replace:
    assert inits_with_data[i].name == inits[i].name
    assert inits_with_data[ref_i].name == inits[ref_i].name
    assert i > ref_i

    _lowerCAmelCase	      =					inits[i].name
    _lowerCAmelCase	      =					inits[ref_i].name

    model_without_ext.graph.initializer.remove(inits[i]					)

    # for n in model.graph.node:
    _graph_replace_input_with(model_without_ext.graph,		UpperCAmelCase_,		UpperCAmelCase_					)







def     A__			(       __lowerCamelCase					):





  """simple docstring"""
  _lowerCAmelCase	      =					os.path.dirname(UpperCAmelCase_					)
  _lowerCAmelCase	      =					os.path.basename(UpperCAmelCase_					)

  _lowerCAmelCase	      =					onnx.load(os.path.join(UpperCAmelCase_,		UpperCAmelCase_					)					)

  _lowerCAmelCase	      =					list(model.graph.initializer					)

  _lowerCAmelCase	      =					set()
  _lowerCAmelCase	      =					{}
  _lowerCAmelCase	      =					[]

  _lowerCAmelCase	      =					0

  for i in range(len(UpperCAmelCase_					)					):
    if i in dup_set:
      continue

    for j in range(i + 1,		len(UpperCAmelCase_					)					):
      if j in dup_set:
        continue
      if _is_equal_tensor_proto(inits[i],		inits[j]					):
        dup_set.add(UpperCAmelCase_					)
        dup_set.add(UpperCAmelCase_					)

        _lowerCAmelCase	      =					inits[j].data_type
        _lowerCAmelCase	      =					numpy.prod(inits[j].dims					)
        if dtype == 1:
          mem_size *= 4
        elif dtype == 6:
          mem_size *= 4
        elif dtype == 7 or dtype == 1_1:
          mem_size *= 8
        else:
          print('unexpected data type: ',		UpperCAmelCase_					)
        total_reduced_size += mem_size

        _lowerCAmelCase	      =					inits[i].name
        _lowerCAmelCase	      =					inits[j].name

        if name_i in dup_map:
          dup_map[name_i].append(UpperCAmelCase_					)
        else:
          _lowerCAmelCase	      =					[name_j]
        ind_to_replace.append((j, i)					)

  print('total reduced size: ',		total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4,		'GB'					)

  _lowerCAmelCase	      =					sorted(UpperCAmelCase_					)
  _remove_dup_initializers_from_model(UpperCAmelCase_,		UpperCAmelCase_,		UpperCAmelCase_					)

  _lowerCAmelCase	      =					'optimized_' + model_file_name
  _lowerCAmelCase	      =					os.path.join(UpperCAmelCase_,		UpperCAmelCase_					)
  onnx.save(UpperCAmelCase_,		UpperCAmelCase_					)

  return new_model





"""simple docstring"""


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       List[str]				=      ['''image_processor''', '''tokenizer''']
       UpperCamelCase_		:       Dict				=      '''BlipImageProcessor'''
       UpperCamelCase_		:       Optional[int]				=      '''AutoTokenizer'''






       def __init__(	self			:				List[str]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				Optional[int]      )->	Union[str, Any]:







              """simple docstring"""
              super().__init__(UpperCAmelCase_							,   UpperCAmelCase_      )

              # add QFormer tokenizer
              UpperCamelCase       			=		qformer_tokenizer






       def __call__(	self			:				Tuple							,   UpperCAmelCase_			:				ImageInput = None							,   UpperCAmelCase_			:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,   UpperCAmelCase_			:				bool = True							,   UpperCAmelCase_			:				Union[bool, str, PaddingStrategy] = False							,   UpperCAmelCase_			:				Union[bool, str, TruncationStrategy] = None							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				int = 0							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = True							,   UpperCAmelCase_			:				Optional[Union[str, TensorType]] = None							,   **UpperCAmelCase_			:				str							,   )->	BatchFeature:







              """simple docstring"""
              if images is None and text is None:
                     raise ValueError("You have to specify at least images or text."      )

              UpperCamelCase       			=		BatchFeature()

              if text is not None:
                     UpperCamelCase       			=		self.tokenizer(
                         text=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   padding=UpperCAmelCase_							,   truncation=UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   stride=UpperCAmelCase_							,   pad_to_multiple_of=UpperCAmelCase_							,   return_attention_mask=UpperCAmelCase_							,   return_overflowing_tokens=UpperCAmelCase_							,   return_special_tokens_mask=UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_							,   return_length=UpperCAmelCase_							,   verbose=UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_							,   )
                     encoding.update(UpperCAmelCase_      )
                     UpperCamelCase       			=		self.qformer_tokenizer(
                         text=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   padding=UpperCAmelCase_							,   truncation=UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   stride=UpperCAmelCase_							,   pad_to_multiple_of=UpperCAmelCase_							,   return_attention_mask=UpperCAmelCase_							,   return_overflowing_tokens=UpperCAmelCase_							,   return_special_tokens_mask=UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_							,   return_length=UpperCAmelCase_							,   verbose=UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_							,   )
                     UpperCamelCase       			=		qformer_text_encoding.pop("input_ids"      )
                     UpperCamelCase       			=		qformer_text_encoding.pop("attention_mask"      )

              if images is not None:
                     UpperCamelCase       			=		self.image_processor(UpperCAmelCase_							,   return_tensors=UpperCAmelCase_      )
                     encoding.update(UpperCAmelCase_      )

              return encoding






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   *UpperCAmelCase_			:				Optional[int]							,   **UpperCAmelCase_			:				List[str]      )->	Dict:







              """simple docstring"""
              return self.tokenizer.batch_decode(*UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   *UpperCAmelCase_			:				Dict							,   **UpperCAmelCase_			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              return self.tokenizer.decode(*UpperCAmelCase_							,   **UpperCAmelCase_      )






       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		self.tokenizer.model_input_names
              UpperCamelCase       			=		self.image_processor.model_input_names
              return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				Tuple							,   **UpperCAmelCase_			:				Tuple      )->	Union[str, Any]:







              """simple docstring"""
              if os.path.isfile(UpperCAmelCase_      ):
                     raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"      )
              os.makedirs(UpperCAmelCase_							,   exist_ok=UpperCAmelCase_      )
              UpperCamelCase       			=		os.path.join(UpperCAmelCase_							,   "qformer_tokenizer"      )
              self.qformer_tokenizer.save_pretrained(UpperCAmelCase_      )
              return super().save_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )






       @classmethod
       def 						_SCREAMING_SNAKE_CASE							(	cls			:				Dict							,   UpperCAmelCase_			:				int							,   **UpperCAmelCase_			:				int      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		AutoTokenizer.from_pretrained(UpperCAmelCase_							,   subfolder="qformer_tokenizer"      )
              UpperCamelCase       			=		cls._get_arguments_from_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )
              args.append(UpperCAmelCase_      )
              return cls(*UpperCAmelCase_      )





